Machine learning refers to techniques for using computing systems to train predictive models that use past training examples to predict the outcome of future events that are similarly situated as the training examples. For example, machine learning can be used to train a predictive model, or for brevity, model, that predicts the market value of a house given particular attributes of the house (e.g., square footage, ZIP code, etc.). The attributes are referred to as features of the model. A collection of features associated with a single data point used to train the model is referred to as a training example.
In some examples, data that is generated by one or more devices is processed through one or more models in a cognitive computing environment. For example, a device can generate data (e.g., an image, a video, audio), which is processed by one or more features of the model to provide an output. Frequently, data (e.g., images, video, audio) are captured from mobile devices and/or remote devices. Information brought back from the devices is typically limited to the content of the sensor collection itself, as well as metadata associated with the data. In some examples, the information is received at a back-end system, which process the data based on one or more models to provide one or more results.